Minimax powerful functional analysis of covariance tests with application to longitudinal genome‐wide association studies
针对纵向全基因组关联研究中不规则时间点的稀疏功能数据，提出一种基于看似无关核平滑器的非参数检验方法，能控制环境协变量混杂效应并提升检验功效，应用于ADNI数据库发现了与阿尔茨海默病相关的新基因。
We model the Alzheimer's Disease-related phenotype response variables observed on irregular time points in longitudinal Genome-Wide Association Studies as sparse functional data and propose nonparametric test procedures to detect functional genotype effects while controlling the confounding effects of environmental covariates. Our new functional analysis of covariance tests are based on a seemingly unrelated kernel smoother, which takes into account the within-subject temporal correlations, and thus enjoy improved power over existing functional tests. We show that the proposed test combined with a uniformly consistent nonparametric covariance function estimator enjoys the Wilks phenomenon and is minimax most powerful. Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where an application of the proposed test lead to the discovery of new genes that may be related to Alzheimer's Disease.